Artificial intelligence Portal

For the strong AI hypothesis, see philosophy of artificial intelligence

Strong AI is a term used by futurists, science fiction writers and forward looking researchers to describe artificial intelligence that matches or exceeds human intelligence.^[1] Strong AI is also referred to as the ability to perform "general intelligent action",^[2] or as "artificial general intelligence",^[3] "artificial consciousness", "sentience", "sapience", "self-awareness" or "consciousness"^[4] (although there are subtle differences in the use of each of these terms). Image File history File links Portal. ... The philosophy of artificial intelligence concerns questions of artificial intelligence (AI) such as: What is intelligence? How can one recognize its presence and applications? Is it possible for machines to exhibit intelligence? Does the presence of human-like intelligence imply consciousness and emotions? Is creating human-like artificial intelligence moral... The philosophy of artificial intelligence concerns questions of artificial intelligence (AI) such as: What is intelligence? How can one recognize its presence and applications? Is it possible for machines to exhibit intelligence? Does the presence of human-like intelligence imply consciousness and emotions? Is creating human-like artificial intelligence moral... Futurists is a term often used to describe management consultants who advise corporations on a wide range of global trends, risk management and potential market opportunities. ... Science fiction is a form of speculative fiction principally dealing with the impact of imagined science and technology, or both, upon society and persons as individuals. ... AI redirects here. ... Intelligence is the mental capacity to reason, plan, solve problems, think abstractly, comprehend ideas and language, and learn. ... Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness, is a field related to artificial intelligence and cognitive robotics whose aim is to define that which would have to be synthesized were consciousness to be found in an engineered artifact. ...

Some references classify artificial intelligence research into "strong AI, applied AI and cognitive simulation."^[5] Applied AI (also called "narrow AI"^[1] or "weak AI"^[6]) refers to the use of software to study or accomplish specific problem solving or reasoning tasks that do not encompass (or in some cases, are completely outside of) the full range of human cognitive abilities. AI redirects here. ... Problem solving forms part of thinking. ... Reasoning is the mental (cognitive) process of looking for reasons to support beliefs, conclusions, actions or feelings. ...

History

Origin of the term

See also: Philosophy of artificial intelligence

The term "strong AI" was adopted from the name of an argument in the philosophy of artificial intelligence first identified by John Searle in 1980.^[7] He wanted to distinguish between two different hypotheses about artificial intelligence:^[8] The philosophy of artificial intelligence concerns questions of artificial intelligence (AI) such as: What is intelligence? How can one recognize its presence and applications? Is it possible for machines to exhibit intelligence? Does the presence of human-like intelligence imply consciousness and emotions? Is creating human-like artificial intelligence moral... The philosophy of artificial intelligence concerns questions of artificial intelligence (AI) such as: What is intelligence? How can one recognize its presence and applications? Is it possible for machines to exhibit intelligence? Does the presence of human-like intelligence imply consciousness and emotions? Is creating human-like artificial intelligence moral... John Rogers Searle (born July 31, 1932 in Denver, Colorado) is the Slusser Professor of Philosophy at the University of California, Berkeley, and is noted for contributions to the philosophy of language, philosophy of mind and consciousness, on the characteristics of socially constructed versus physical realities, and on practical reason. ...

• An artificial intelligence system can think and have a mind.^[9]
• An artificial intelligence system can (only) act like it thinks and has a mind.

The first one is called "the strong AI hypothesis" and the second is "the weak AI hypothesis" because the first one makes the stronger statement: it assumes something special has happened to the machine that goes beyond all its abilities that we can test. Searle referred to the "strong AI hypothesis" as "strong AI". This usage, which is fundamentally different than the subject of this article, is common in academic AI research and textbooks.^[10]

The term "strong AI" is now used to describe any artificial intelligence system that acts like it has a mind,^[1] regardless of whether a philosopher would be able to determine if it actually has a mind or not. As Russell and Norvig write: "Most AI researchers take the weak AI hypothesis for granted, and don't care about the strong AI hypothesis."^[11] AI researchers are interested in a related statement (that some sources confusingly call "the strong AI hypothesis"):^[12] Stuart Russell (born 1962) is a computer scientist known for his contributions to artificial intelligence. ... Peter Norvig is currently the Director of Research (formerly Director of Search Quality) at Google Inc. ...

• An artificial intelligence system can think (or act like it thinks) as well or better than people do.

This assertion, which hinges on the breadth and power of machine intelligence, is the subject of this article.

Strong AI research

See also: History of artificial intelligence and AI Winter

Hal-9000 from Stanley Kubrick's 2001: A Space Odyssey, who accurately represented what early AI researchers thought they would accomplish by 2001

Modern AI research began in the middle 50s.^[13] The first generation of AI researchers were convinced that strong AI was possible and that it would exist in just a few decades. As AI pioneer Herbert Simon wrote in 1965: "machines will be capable, within twenty years, of doing any work a man can do."^[14] Their predictions were the inspiration for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who accurately embodied what AI researchers believed they could create. Artificial Intelligence was founded in the early 1950s by an eclectic group of visionaries who claimed to be on the verge of changing the world and mans place in it. ... To meet Wikipedias quality standards, this article may require cleanup. ... Image File history File links No higher resolution available. ... Image File history File links No higher resolution available. ... HAL 9000 (Heuristically programmed ALgorithmic computer) is a fictional character in Arthur C. Clarkes Space Odyssey saga. ... Kubrick redirects here. ... Herbert Alexander Simon (June 15, 1916 – February 9, 2001) was an American political scientist whose research ranged across the fields of cognitive psychology, computer science, public administration, economics, management, and philosophy of science and a professor, most notably, at Carnegie Mellon University. ... Kubrick redirects here. ... Wikiquote has a collection of quotations related to: Arthur C. Clarke Sir Arthur Charles Clarke, CBE (born 16 December 1917) is a British science-fiction author and inventor, most famous for his novel 2001: A Space Odyssey, and for collaborating with director Stanley Kubrick on the film of the same... HAL 9000 (Heuristically programmed ALgorithmic computer) is a fictional character in Arthur C. Clarkes Space Odyssey saga. ...

However, in the early 70s, it became obvious that researchers had grossly underestimated the difficulty of the project. The agencies that funded AI became skeptical of strong AI and put researchers under increasing pressure to produce useful technology, or "applied AI".^[15] As the eighties began, Japan's fifth generation computer project revived interest in strong AI, setting out a ten year timeline that included strong AI goals like "carry on a casual conversation".^[16] In response to this and the success of expert systems, both industry and government pumped money back into the field.^[17] However, the market for AI spectacularly collapsed in the late 80s and the goals of the fifth generation computer project were never fulfilled.^[18] For the second time in 20 years, AI researchers who had predicted the imminent arrival of strong AI had been shown to fundamentally mistaken about what they could accomplish. The Fifth Generation Computer Systems project (FGCS) was an initiative by Japans Ministry of International Trade and Industry, begun in 1982, to create a fifth generation computer (see history of computing hardware) which was supposed to perform much calculation utilizing massive parallelism. ... An expert system is a class of computer programs developed by researchers in artificial intelligence during the 1970s and applied commercially throughout the 1980s. ...

By the 1990s, AI researchers had gained a reputation for making promises they could not keep. Many AI researchers today are reluctant to make any kind of prediction at all^[19] and avoid any mention of "human level" artificial intelligence, for fear of being labeled a "wild-eyed dreamer."^[20] This is an unfortunate consequence of developing nascent technologies. For the most part, researchers today choose to focus on specific sub-problems where they can produce verifiable results and commercial applications, such as neural nets, computer vision or data mining,^[21] and most believe that these sub-problems must be solved before machines with strong AI can exist.^[22] Interest in direct research into strong AI tends to come from outside the field, from internet entrepreneurs (such as Jeff Hawkins) or from futurists such as Ray Kurzweil. A neural network is an interconnected group of neurons. ... Computer vision is the science and technology of machines that see. ... Data mining is the principle of sorting through large amounts of data and picking out relevant information. ... Jeff Hawkins (born June 1, 1957 in Huntington, New York) is the founder of Palm Computing (where he invented the Palm Pilot) [1] and Handspring (where he invented the Treo). ... Dr. Raymond Kurzweil (born February 12, 1948) is a pioneer in the fields of optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic musical keyboards. ...

Defining strong AI

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A computer enters the framework of strong AI if a machine approaches or supersedes human intelligence, if it can do typically human tasks, if it can apply a wide range of background knowledge and has some degree of self-consciousness. John McCarthy stated in his work What is AI? that we still do not have a solid definition of intelligence. Human-bound definitions of measurable intelligence, like IQ, cannot easily be applied to machine intelligence. Image File history File links This is a lossless scalable vector image. ... Image File history File links Emblem-important. ... For other uses, see Intelligence (disambiguation). ... John McCarthy (born September 4, 1927, in Boston, Massachusetts, sometimes known affectionately as Uncle John McCarthy), is a prominent computer scientist who received the Turing Award in 1971 for his major contributions to the field of Artificial Intelligence. ... IQ redirects here; for other uses of that term, see IQ (disambiguation). ...

The most famous definition of AI was the operational one proposed by Alan Turing in his "Turing test" proposal. There have been very few attempts to create such definitions since (some of them are in the AI Project) Alan Mathison Turing, OBE, FRS (23 June 1912 – 7 June 1954) was an English mathematician, logician, and cryptographer. ... For the Doctor Who novel named after the test, see The Turing Test (novel). ...

A proposal to define a more easily quantifiable measure of artificial intelligence is:

Intelligence is the possession of a model of reality and the ability to use this model to conceive and plan actions and to predict their outcomes. The higher the complexity and precision of the model, the plans, and the predictions, and the less time needed, the higher is the intelligence.[1]

Research approaches

Artificial general intelligence

Artificial General Intelligence research aims to create AI that can replicate human-level intelligence completely, often called an Artificial General Intelligence (AGI) to distinguish from less ambitious AI projects. As yet, researchers have devoted little attention to AGI, with some claiming that intelligence is too complex to be completely replicated in the near term. Some small groups of computer scientists are doing AGI research, however. Organizations pursuing AGI include the Adaptive AI, Artificial General Intelligence Research Institute (AGIRI) and the Singularity Institute for Artificial Intelligence. One recent addition is Numenta, a project based on the theories of Jeff Hawkins, the creator of the Palm Pilot. While Numenta takes a computational approach to general intelligence, Hawkins is also the founder of the RedWood Neuroscience Institute, which explores conscious thought from a biological perspective. Founded in 2001, the Artificial General Intelligence Research Institutes (AGIRI) mission is to foster the creation of powerful and ethically positive Artificial General Intelligence. ... The Singularity Institute for Artificial Intelligence is a non-profit organization with the goal of developing a theory of Friendly artificial intelligence and implementing that theory as a software system. ... Wikinews has news related to this article: New company to research artificial brain Company founded March 24, 2005, by Palm founder Jeff Hawkins with his longtime business partner Donna Dubinsky and Stanford graduate student Dileep George. ... Jeff Hawkins (born June 1, 1957 in Huntington, New York) is the founder of Palm Computing (where he invented the Palm Pilot) [1] and Handspring (where he invented the Treo). ... An early model - the Pilot 5000 The Palm m130 was one of the first Palms with a colour screen Pilot was the name given to the first generation of personal digital assistants manufactured by Palm Computing in 1996 (then a division of U.S. Robotics and later 3Com). ...

Simulated human brain model

This is seen by many^{[attribution needed]} as the quickest means of achieving strong AI, as it doesn't require complete understanding of how intelligence works. Basically, a very powerful computer would simulate a human brain, often in the form of a network of neurons. For example, given a map of all (or most) of the neurons in a functional human brain, and a good understanding of how a single neuron works, it would be possible for a computer program to simulate the working brain over time. Given some method of communication, this simulated brain might then be shown to be fully intelligent. The exact form of the simulation varies: instead of neurons, a simulation might use groups of neurons, or alternatively, individual molecules might be simulated. It's also unclear which portions of the human brain would need to be modeled: humans can still function while missing portions of their brains, and areas of the brain are associated with activities (such as breathing) that might not be necessary to think.^{[citation needed]}

This approach would require three things:

The RIKEN MDGRAPE-3 supercomputer

• Hardware. An extremely powerful computer would be required for such a model. Futurist Ray Kurzweil estimates 10 million MIPS, or ten petaflops. At least one special-purpose petaflops computer has already been built (the Riken MDGRAPE-3) and there are nine current computing projects (such as BlueGene/P) to build more general purpose petaflops computers all of which should be completed by 2008, if not sooner.[2] Most other attempted estimates of the brain's computational power equivalent have been rather higher, ranging from 100 million MIPS to 100 billion MIPS. Using Moore's Law, it might be estimated that an optimistic prediction of when such levels of computing power might be reached to be by ~2015 (for 10 petaflops), up to a more conservative estimate of ~2040 (for 100,000 petaflops) and, finally, to reach the levels estimated by the Blue Brain project of a complete simulation of a human neocortex, a general purpose CPU-based supercomputer should reach a sufficient level of processing power by ~2060. However, considering that GPU processing and Stream Processing power appears to double every year, these estimates will be reached much sooner using GPGPU processing as high-end GPU's set to arrive in early 2008 are already going to be able to process over 1 teraflop, which is 20x more powerful than a standard quad-core CPU. Based on this information, one might estimate that GPU's can attain the required level of performance based on Blue Brain estimates by ~2030. It should also be noted, however, that the overhead introduced by the modeling of the biological details of neural behaviour might require a simulator to have access to computational power much greater than that of the brain itself, although the Blue Brain project is set to address neuro-chemical functioning on a molecular scale.
• Software. Software to simulate the function of a brain would be required. This assumes that the human mind is the central nervous system and is governed by physical laws. Constructing the simulation would require a great deal of knowledge about the physical and functional operation of the human brain, and might require detailed information about a particular human brain's structure. Information would be required both of the function of different types of neurons, and of how they are connected. Note that the particular form of the software dictates the hardware necessary to run it. For example, an extremely detailed simulation including molecules or small groups of molecules would require enormously more processing power than a simulation that models neurons using a simple equation, and a more accurate model of a neuron would be expected to be much more expensive computationally than a simple model. The more neurons in the simulation, the more processing power it would require.
• Understanding. Finally, it requires sufficient understanding thereof to be able to model it mathematically. This could be done either by understanding the central nervous system, or by mapping and copying it. Neuroimaging technologies are improving rapidly, and Kurzweil predicts that a map of sufficient quality will become available on a similar timescale to the required computing power. However, the simulation would also have to capture the detailed cellular behaviour of neurons and glial cells, presently only understood in the broadest of outlines.

Once such a model is built, it will be easily altered and thus open to trial-and-error experimentation. This is likely to lead to huge advances in understanding, allowing the model's intelligence to be improved/motivations altered.^{[dubious – discuss]} Image File history File links Metadata No higher resolution available. ... Image File history File links Metadata No higher resolution available. ... Future studies reflects on how today’s changes (or the lack thereof) become tomorrow’s reality. ... Dr. Raymond Kurzweil (born February 12, 1948) is a pioneer in the fields of optical character recognition (OCR), text-to-speech synthesis, speech recognition technology, and electronic musical keyboards. ... Look up million in Wiktionary, the free dictionary. ... Instructions per second (IPS) is a measure of a computers processor speed. ... In computing, FLOPS is an abbreviation of FLoating point Operations Per Second. ... The RIKEN MDGRAPE-3 supercomputer For the astronomical supercomputer also known as GRAPE, see Gravity Pipe. ... This article is about the supercomputer. ... Gordon Moores original graph from 1965 Growth of transistor counts for Intel processors (dots) and Moores Law (upper line=18 months; lower line=24 months) For the observation regarding information retrieval, see Mooers Law. ... Blue Brain is a project to begin the construction of a simulated brain. ... GPU may stand for: Graphics processing unit, a special stream processor used in computer graphics hardware Gosudarstvennoye Politicheskoye Upravlenie (Главное Политическое Управление, or Main Political Directorate) of the Red Army, responsible for troops morale and propaganda. ... For other uses, see Event Stream Processing. ... General-purpose computing on graphics processing units (GPGPU, also referred to as GPGP and to a lesser extent GP²) is a recent trend focused on using GPUs to perform computations rather than the CPU. The addition of programmable stages and higher precision arithmetic to the rendering pipelines allowed software developers... Blue Brain is a project to begin the construction of a simulated brain. ... A diagram showing the CNS: 1. ... For a list of set rules, see Laws of science. ... It has been suggested that this article or section be merged with functional neuroimaging. ... Neuroglia cells of the brain shown by Golgis method. ...

The Blue Brain project aims to use one of the fastest supercomputer architectures in the world, IBM's Blue Gene platform, to simulate a single neocortical column consisting of approximately 60,000 neurons and 5km of interconnecting synapses. The eventual goal of the project is to use supercomputers to simulate an entire brain. Blue Brain is a project to begin the construction of a simulated brain. ... For other uses, see IBM (disambiguation) and Big Blue. ... This article is about the supercomputer. ... The neocortex (Latin for new bark or new rind) is a part of the brain of mammals. ...

The brain gets its power from performing many parallel operations, a standard computer from performing operations very quickly.

The human brain has roughly 100 billion neurons operating simultaneously, connected by roughly 100 trillion synapses.^[23] By comparison, a modern computer microprocessor uses only 1.7 billion transistors.[3] Although estimates of the brain's processing power put it at around 10¹⁴ neuron updates per second,^[24] it is expected that the first unoptimized simulations of a human brain will require a computer capable of 10¹⁸ FLOPS. By comparison a general purpose CPU (circa 2006) operates at a few GFLOPS (10⁹ FLOPS). (each FLOP may require as many as 20,000 logic operations). A microprocessor is a programmable digital electronic component that incorporates the functions of a central processing unit (CPU) on a single semiconducting integrated circuit (IC). ... For other uses, see Flop. ...

However, a neuron is estimated to spike 200 times per second (this giving an upper limit on the number of operations).^{[citation needed]} Signals between them are transmitted at a maximum speed of 150 meters per second. A modern 2GHz processor operates at 2 billion cycles per second, or 10,000,000 times faster than a human neuron, and signals in electronic computers travel at roughly half the speed of light; faster than signals in human by a factor of 1,000,000.^{[citation needed]} The brain consumes about 20W of power whereas supercomputers may use as much as 1MW or an order of 100,000 more (note: Landauer limit is 3.5x10²⁰ op/sec/watt at room temperature). Landauers Principle, first argued in 1961 by Rolf Landauer of IBM, holds that any logically irreversible manipulation of information, such as the erasure of a bit or the merging of two computation paths, must be accompanied by a corresponding entropy increase in non-information bearing degrees of freedom of...

Neuro-silicon interfaces have also been proposed. [4] [5]

Critics of this approach believe it's possible to achieve AI directly without imitating nature and often use the analogy that early attempts to construct flying machines modeled them after birds, but modern aircraft do not look like birds. The direct approach is used in AI - What is this where it is shown that if we have a formal definition of AI then we can find it by enumerating all possible programs and testing each of them to see is it Artificial Intelligence or not.

Artificial consciousness research

Main article: Artificial consciousness

Please help improve this article or section by expanding it. Further information might be found on the talk page or at requests for expansion. (October 2007)

Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness, is a field related to artificial intelligence and cognitive robotics whose aim is to define that which would have to be synthesized were consciousness to be found in an engineered artifact. ... Image File history File links This is a lossless scalable vector image. ...

Emergence

Main article: emergence

Some^{[attribution needed]} have suggested that intelligence can arise as an emergent quality from the convergence of random, man-made technologies. Human sentience — or any other biological and naturally occurring intelligence — arises out of the natural process of species evolution and an individual's experiences. Discussion of this eventuality is currently limited to fiction and theory.^{[citation needed][original research?]} A termite cathedral mound produced by a termite colony: a classic example of emergence in nature. ... Science fiction is a form of speculative fiction principally dealing with the impact of imagined science and technology, or both, upon society and persons as individuals. ...

See also

• History of artificial intelligence
• Technological singularity aka "The Singularity"
• Singularity Institute for Artificial Intelligence

Artificial Intelligence was founded in the early 1950s by an eclectic group of visionaries who claimed to be on the verge of changing the world and mans place in it. ... When plotted on a logarithmic graph, 15 separate lists of paradigm shifts for key events in human history show an exponential trend. ... The Singularity Institute for Artificial Intelligence is a non-profit organization with the goal of developing a theory of Friendly artificial intelligence and implementing that theory as a software system. ...

External links

• Expanding Frontiers of Humanoid Robots
• AI lectures from Tokyo hosted by Rolf Pfeifer
• Artificial General Intelligence Research Institute
• The Genesis Group at MIT's CSAIL — Modern research on the computations that underlay human intelligence
• Essentials of general intelligence, article at Adaptive AI.
• Problems with Thinking Robots
• www.cs.utoronto.ca
• www.otterbein.edu lecture notes
• www.senapps.com
• www.eng.warwick.ac.uk
• www.thetechzone.com, Article - Looks at new modelling ideas such as fuzzy logic and quantum inspired parallel distributed networks.

Notes

The physical symbol system hypothesis was formulated by Newell and Simon (1963) as the result of success of GPS (General Problem Solver) and subsequent programs as models of cognition. ... Psychological science redirects here. ... Drawing of the cells in the chicken cerebellum by S. Ramón y Cajal Neuroscience is a field that is devoted to the scientific study of the nervous system. ... Cognitive science is usually defined as the scientific study either of mind or of intelligence (e. ... A Phrenological mapping of the brain. ... A phrenological mapping of the brain. ... The Lighthill report (1973) formed the basis for the decision by the British government to end support for AI research in all but two universities [from AIAMA]. The report stated that AI researchers had failed to address the issue of combinatorial explosion when solving problems within real world domains. ... The Defense Advanced Research Projects Agency (DARPA) is an agency of the United States Department of Defense responsible for the development of new technology for use by the military. ... John McCarthy (born September 4, 1927, in Boston, Massachusetts, sometimes known affectionately as Uncle John McCarthy), is a prominent computer scientist who received the Turing Award in 1971 for his major contributions to the field of Artificial Intelligence. ... Year 2007 (MMVII) is the current year, a common year starting on Monday of the Gregorian calendar and the AD/CE era in the 21st century. ... is the 211th day of the year (212th in leap years) in the Gregorian calendar. ... Hans Moravec (born November 30, 1948 in Austria) is a research professor at the Robotics Institute (Carnegie Mellon) of Carnegie Mellon University. ... Ex-Virginia and Truckee Railroad No. ... The Encyclopædia Britannica is a general English-language encyclopaedia published by Encyclopædia Britannica, Inc. ...

References

• Crevier, Daniel (1993), AI: The Tumultuous Search for Artificial Intelligence, New York, NY: BasicBooks, ISBN 0-465-02997-3
• Feigenbaum, Edward A. & McCorduck, Pamela (1983), The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World, Michael Joseph, ISBN 0-7181-2401-4
• Howe, J. (November 1994), Artificial Intelligence at Edinburgh University : a Perspective, <http://www.dai.ed.ac.uk/AI_at_Edinburgh_perspective.html>
• Kurzweil, Ray (2005), The Singularity is Near, Viking Press
• Lighthill, Professor Sir James (1973), "Artificial Intelligence: A General Survey", Artificial Intelligence: a paper symposium, Science Research Council
• Moravec, Hans (1976), The Role of Raw Power in Intelligence, <http://www.frc.ri.cmu.edu/users/hpm/project.archive/general.articles/1975/Raw.Power.html>
• Moravec, Hans (1988), Mind Children, Harvard University Press
• Newell, Allen & Simon, H. A. (1963), "GPS: A Program that Simulates Human Thought", in Feigenbaum, E.A. & Feldman, J., Computers and Thought, McGraw-Hill
• NRC (1999), "Developments in Artificial Intelligence", Funding a Revolution: Government Support for Computing Research, National Academy Press
• Russell, Stuart J. & Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, NJ: Prentice Hall, ISBN 0-13-790395-2, <http://aima.cs.berkeley.edu/>
• Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences 3 (3): 417-457, <http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html>
• Simon, H. A. (1965), The Shape of Automation for Men and Management, New York: Harper & Row
• Voss, Peter (2006), Goertzel, Ben & Pennachin, Cassio, eds., Essentials of general intelligence Artificial General Intelligence, Springer, ISBN 3-540-23733-X, <http://www.adaptiveai.com/research/index.htm#different_approach Essentials of general intelligence>